Python toolkit for data science

• pandas: to import, export, and transform data from files, including .csv, .xlsx and json files

• open(), read(), write(), close(): : to execute create, read, update, delete (CRUD) actions

Web scraping

• Requests: to make HTTP requests such as GET and POST

• BeautifulSoup: to web scrape data from most web pages

• Selenium: to collect data from complex web pages

• Scrapy: to acquire data in a scalable manner

PDF reports

• Traditional libraries: PyPDF2, pdfminer, tika, PyMuPDF and fitz

• OCR libraries: textract and pytesseract

2. Data preparation and analysis

Data preparation and analysis methods depend on the type of data:

• Structured data: Tabular data

• Unstructured data: Text, audio and images

Data preparation

• pandas: to perform data transformations, aggregations, validations and cleansing

• numpy: to carry out fast numerical dataset operations

• nltk: to perform a wider range of NLP pre-processing functionality

Data analysis

• SciPy: to perform different mathematical computations

• scikit-learn: the most commonly used library for predictive analytics

• nltk: to perform NLP analysis, including sentiment scoring

• spacy: to classify entity names through entity recognition modelling

• textblob and vaderSentiment: to perform sentiment analysis on textual data

• keras: to build and transform deep-learning networks and datasets

• pytorch: to build advanced and flexible deep-learning models

• tensorflow: another advanced framework to build deep-learning models

• word2vec, glove, BERT and USE: pre-trained embedded language models to build advanced NLP

• huggingface: open source repository of many different kinds of NLP pre-trained models

• matplotlib, seaborn, and plotly: to visualise data through graphs

3. Solution packaging

• jupyter notebook: to rapidly code, visualise and present data applications and reports

• powerbiclient: to visualise data insights and predictions

• dash: to build graphical and user interface web page components

4. IDE

• PyCharm, Visual Studio Code, jupyterlab and Spyder are the IDEs most commonly used by Python users. These IDEs come with a feature to easily perform version-control operations.

Conclusion

Open-source programmers around the world are constantly improvising Python and open-source libraries and tools, making the language really powerful, easy to use and agile. With leading financial institutions and corporations embracing hybrid cloud strategies to manage work-flow during the pandemic and beyond, Python has become an integral catalyst for transforming global businesses.

Bibliography

https://venturebeat.com/2020/12/02/github-python-and-typescript-gain-popularity-among-programming-languages/

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Sreeram Ramesh

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Sreeram Ramesh has over six years of experience in solving business problems using machine learning (ML), deep learning and statistical techniques. He has worked with multiple financial service providers and Fortune 500 companies, providing robust data science solutions. At Acuity Knowledge Partners, Sreeram creates artificial intelligence (AI)/ML engines to analyse textual data, and generate insights and proprietary scores. He holds a Bachelor of Engineering degree in Mechanical Engineering from Birla Institute of Technology and Science (BITS), Pilani.